feat: 多平台 Coding Plan 统一管理系统初始实现
- 支持 MiniMax/OpenAI/Google Gemini/智谱/Kimi 五个平台 - 插件化 Provider 架构，自动发现注册 - 多维度 QuotaRule 额度追踪（固定间隔/自然周期/API同步/手动） - OpenAI + Anthropic 兼容 API 代理，SSE 流式转发 - Model 路由表 + 额度耗尽自动 fallback - 多媒体任务队列（图片/语音/视频） - Vue3 + Tailwind 单文件 Web 仪表盘 - Docker 一键部署 Made-with: Cursor
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锦麟 王
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"""API 代理路由 -- OpenAI / Anthropic 兼容端点"""
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from __future__ import annotations
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import json
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import time
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from typing import Any
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from fastapi import APIRouter, Header, HTTPException, Request
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from fastapi.responses import StreamingResponse
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from app import database as db
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from app.config import settings
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from app.providers import ProviderRegistry
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router = APIRouter()
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def _verify_key(authorization: str | None):
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expected = settings.server.proxy_api_key
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if not expected or expected == "sk-plan-manage-change-me":
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return # 未配置则跳过鉴权
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if not authorization:
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raise HTTPException(401, "Missing Authorization header")
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token = authorization.removeprefix("Bearer ").strip()
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if token != expected:
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raise HTTPException(403, "Invalid API key")
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async def _resolve_plan(model: str, plan_id_header: str | None) -> tuple[dict, str]:
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"""解析目标 Plan: 优先 X-Plan-Id header, 否则按 model 路由表查找"""
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if plan_id_header:
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plan = await db.get_plan(plan_id_header)
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if not plan:
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raise HTTPException(404, f"Plan {plan_id_header} not found")
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return plan, model
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resolved_plan_id = await db.resolve_model(model)
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if not resolved_plan_id:
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raise HTTPException(404, f"No plan found for model '{model}'")
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plan = await db.get_plan(resolved_plan_id)
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if not plan:
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raise HTTPException(500, "Resolved plan missing from DB")
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return plan, model
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async def _stream_and_count(provider, messages, model, plan, stream, **kwargs):
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"""流式转发并统计 token 消耗"""
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total_tokens = 0
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async for chunk_data in provider.chat(messages, model, plan, stream=stream, **kwargs):
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yield chunk_data
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# 尝试从 chunk 中提取 usage
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if not stream and chunk_data:
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try:
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resp_obj = json.loads(chunk_data)
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usage = resp_obj.get("usage", {})
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total_tokens = usage.get("total_tokens", 0)
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except (json.JSONDecodeError, TypeError):
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pass
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# 流式模式下无法精确统计 token，按请求次数 +1 计费
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await db.increment_quota_used(plan["id"], token_count=total_tokens)
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# ── OpenAI 兼容: /v1/chat/completions ─────────────────
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@router.post("/v1/chat/completions")
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async def openai_chat_completions(
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request: Request,
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authorization: str | None = Header(None),
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x_plan_id: str | None = Header(None, alias="X-Plan-Id"),
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):
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_verify_key(authorization)
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body = await request.json()
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model = body.get("model", "")
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messages = body.get("messages", [])
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stream = body.get("stream", False)
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if not model or not messages:
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raise HTTPException(400, "model and messages are required")
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plan, model = await _resolve_plan(model, x_plan_id)
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# 检查额度
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if not await db.check_plan_available(plan["id"]):
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raise HTTPException(429, f"Plan '{plan['name']}' quota exhausted")
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provider = ProviderRegistry.get(plan["provider_name"])
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if not provider:
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raise HTTPException(500, f"Provider '{plan['provider_name']}' not registered")
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extra_kwargs = {k: v for k, v in body.items()
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if k not in ("model", "messages", "stream")}
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if stream:
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return StreamingResponse(
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_stream_and_count(provider, messages, model, plan, True, **extra_kwargs),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "X-Plan-Id": plan["id"]},
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)
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else:
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chunks = []
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async for c in _stream_and_count(provider, messages, model, plan, False, **extra_kwargs):
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chunks.append(c)
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result = json.loads(chunks[0]) if chunks else {}
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return result
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# ── Anthropic 兼容: /v1/messages ──────────────────────
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@router.post("/v1/messages")
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async def anthropic_messages(
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request: Request,
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authorization: str | None = Header(None),
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x_plan_id: str | None = Header(None, alias="X-Plan-Id"),
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x_api_key: str | None = Header(None, alias="x-api-key"),
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):
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auth = authorization or (f"Bearer {x_api_key}" if x_api_key else None)
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_verify_key(auth)
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body = await request.json()
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model = body.get("model", "")
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messages = body.get("messages", [])
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stream = body.get("stream", False)
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system_msg = body.get("system", "")
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if not model or not messages:
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raise HTTPException(400, "model and messages are required")
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# Anthropic 格式 -> OpenAI 格式 messages
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oai_messages = []
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if system_msg:
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oai_messages.append({"role": "system", "content": system_msg})
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for m in messages:
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content = m.get("content", "")
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if isinstance(content, list):
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# Anthropic 多模态 content blocks -> 取文本
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text_parts = [c.get("text", "") for c in content if c.get("type") == "text"]
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content = "\n".join(text_parts)
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oai_messages.append({"role": m.get("role", "user"), "content": content})
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plan, model = await _resolve_plan(model, x_plan_id)
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if not await db.check_plan_available(plan["id"]):
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raise HTTPException(429, f"Plan '{plan['name']}' quota exhausted")
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provider = ProviderRegistry.get(plan["provider_name"])
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if not provider:
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raise HTTPException(500, f"Provider '{plan['provider_name']}' not registered")
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if stream:
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async def anthropic_stream():
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"""将 OpenAI SSE 格式转换为 Anthropic SSE 格式"""
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yield f"event: message_start\ndata: {json.dumps({'type': 'message_start', 'message': {'id': 'msg_proxy', 'type': 'message', 'role': 'assistant', 'model': model, 'content': []}})}\n\n"
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yield f"event: content_block_start\ndata: {json.dumps({'type': 'content_block_start', 'index': 0, 'content_block': {'type': 'text', 'text': ''}})}\n\n"
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async for chunk_data in _stream_and_count(provider, oai_messages, model, plan, True):
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if chunk_data.startswith("data: [DONE]"):
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break
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if chunk_data.startswith("data: "):
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try:
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oai_chunk = json.loads(chunk_data[6:].strip())
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delta = oai_chunk.get("choices", [{}])[0].get("delta", {})
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text = delta.get("content", "")
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if text:
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yield f"event: content_block_delta\ndata: {json.dumps({'type': 'content_block_delta', 'index': 0, 'delta': {'type': 'text_delta', 'text': text}})}\n\n"
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except (json.JSONDecodeError, IndexError):
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pass
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yield f"event: content_block_stop\ndata: {json.dumps({'type': 'content_block_stop', 'index': 0})}\n\n"
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yield f"event: message_stop\ndata: {json.dumps({'type': 'message_stop'})}\n\n"
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return StreamingResponse(
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anthropic_stream(),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache"},
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)
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else:
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chunks = []
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async for c in _stream_and_count(provider, oai_messages, model, plan, False):
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chunks.append(c)
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oai_resp = json.loads(chunks[0]) if chunks else {}
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# OpenAI 响应 -> Anthropic 响应
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content_text = ""
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choices = oai_resp.get("choices", [])
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if choices:
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content_text = choices[0].get("message", {}).get("content", "")
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usage = oai_resp.get("usage", {})
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return {
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"id": "msg_proxy",
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"type": "message",
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"role": "assistant",
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"model": model,
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"content": [{"type": "text", "text": content_text}],
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"stop_reason": "end_turn",
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"usage": {
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"input_tokens": usage.get("prompt_tokens", 0),
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"output_tokens": usage.get("completion_tokens", 0),
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},
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}
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